Journal of Intelligent & Fuzzy Systems - Volume 43, issue 1

Authors: Pan, Hongguang | Zhang, Huipeng | Lei, Xinyu | Xin, Fangfang | Wang, Zheng

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212740

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1229-1239, 2022

Authors: Haque, Md. Rakibul | Mishu, Sadia Zaman | Palash Uddin, Md. | Al Mamun, Md.

Article Type: Research Article

Abstract: Hyperspectral Image (HSI) is usually composed of hundreds of capturing wavelength bands, which not only increase the size of the HSI rapidly but also impose various obstacles in classifying the objects accurately. Moreover, the traditional machine learning schemes utilize only the spectral features for HSI classification, which, therefore, neglect the spatial features that have a significant impact on the classification improvement. To address the aforementioned issues, in this paper, we propose to employ the principal component analysis (PCA), the baseline feature extraction method, and a thoughtfully designed stacked autoencoder, a deep learning-based feature extraction approach, for reducing the high dimensionality …of the HSI and then propose a novel lightweight 3D-2D convolutional neural network (CNN) framework to concurrently exploit both spatial and spectral features from the dimensionality-reduced HSI for classification. In particular, PCA and stacked autoencoder are applied to reduce the high dimensionality of the original HSI and then the proposed 3D-2D CNN provides a combination of 3D and 2D convolution operations to extract the subtle spatial and spectral features for efficient classification. We well-adjust the proposed 3D-2D CNN architecture, and perform extensive experiments on three benchmark HSI datasets and compare our approach with the state-of-the-art classical and deep learning methods. Experimental results illustrate that we have achieved an overall accuracy of 99.73%, 99.90%, and 99.32% on Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively, which outperform the classical machine learning and independent 2D and 3D CNN-based state-of-the-art methods. Show more

Keywords: Feature extraction, principal component analysis, deep learning, stacked autoencoder, classification, convolutional neural network

DOI: 10.3233/JIFS-212829

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1241-1258, 2022

Authors: Jose, Merin | Mathew, Sunil C.

Article Type: Research Article

Abstract: In this paper, the authors introduce catalyzed LM -G-filter spaces, a special case of weakly inspired LM -G-filter spaces and identify certain properties of these spaces. It is proved that C LM -G , the category of catalyzed LM -G-filter spaces, is isomorphic to I LM -G , the category of inspired LM -G-filter spaces. Moreover, the categorical connection between WI LM -G , the category of weakly inspired LM -G-filter spaces, and C LM -G is investigated through interior and exterior catalyzation of weakly inspired LM -G-filter spaces. It is proved that C LM -G is an …isomorphism-closed, bireflective and bicoreflective full subcategory of WI LM -G and LM -G . Show more

Keywords: Category, functor, reflective subcategory, coreflective subcategory

DOI: 10.3233/JIFS-212923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1259-1269, 2022

Authors: Yan, Zhiwen | Chen, Ying | Wang, Xianqing | Zhu, Jia | Li, Jianbo

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-212994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1271-1283, 2022

Authors: Luo, Hongyun | Lin, Xiangyi | Niu, Yi

Article Type: Research Article

Abstract: This research explores indicators and methods for an enterprise to measure and evaluate user satisfaction with enterprise social media for knowledge management. This paper presents qualitative indicators, including three service levels of enterprise social media for knowledge management (KM) from a techno-social perspective. This research puts forward a synthetic evaluation model mixed with linguistic variables, consistent fuzzy preference relations (CFPR) and cloud model for measuring and evaluating user satisfaction. The synthetic evaluation model can transform linguistic variables into quantitative data to obtain user satisfaction levels and determine the distance between the expected satisfaction level and actual performance. This research can …help an enterprise to improve the service ability of its social media to meet users’ requirements for knowledge management. Show more

Keywords: User satisfaction, enterprise social media, knowledge management, linguistic variables, consistent fuzzy preference relations, cloud model

DOI: 10.3233/JIFS-213026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1285-1297, 2022

Authors: Li, Yan | Guo, Junjun | Yu, Zhengtao | Gao, Shengxiang

Article Type: Research Article

Abstract:  Semantic alignment is a key component in Cross-Language Text Matching (CLTM) to facilitate matching (e.g., query-document matching) between two languages. The current solutions for semantic alignment mainly perform word-level translation directly, without considering the contextual information for the whole query and documents. To this end, we propose a Dual-Level Collaborative Rough-to-Fine Filter Alignment Network (DLCCFA) to achieve better cross-language semantic alignment and document matching. DLCCFA is devised with both a coarse-grained filter in word-level and a fine-grained filter in sentence-level. Concretely, for the query in word-level, we firstly extract top-k translation candidates for each token in the query …through a probabilistic bilingual lexicon. Then, a Translation Probability Attention (TPA) mechanism is proposed to obtain coarse-grained word alignment, which generates the corresponding query auxiliary sentence. Afterwards, we further propose a Bilingual Cross Attention and utilize Self-Attention to achieve fine-grained sentence-level filtering, resulting in the cross-language representation of the query. The idea is that each token in the query works as an anchor to filter the semantic noise in the query auxiliary sentence and accurately align semantics of different languages. Extensive experiments on four real-world datasets of six languages demostrate that our method can outperform the mainstream alternatives of CLTM. Show more

Keywords: Cross-language text matching, Alignment, Probabilistic bilingual lexicon, Translation probability attention, Bilingual cross attention

DOI: 10.3233/JIFS-213070

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1299-1314, 2022

Authors: Lu, Yu | Song, Jingjing | Wang, Pingxin | Xu, Taihua

Article Type: Research Article

Abstract: In the era of big data for exploring attribute reduction/rough set-based feature selection related problems, to design efficient strategies for deriving reducts and then reduce the dimensions of data, two fundamental perspectives of Granular Computing may be taken into account: breaking up the whole into pieces and gathering parts into a whole. From this point of view, a novel strategy named label-specific guidance is introduced into the process of searching reduct. Given a formal description of attribute reduction, by considering the corresponding constraint, we divide it into several label-specific based constraints. Consequently, a sequence of these label-specific based constraints can …be obtained, it follows that the reduct related to the previous label-specific based constraint may have guidance on the computation of that related to the subsequent label-specific based constraint. The thinking of this label-specific guidance runs through the whole process of searching reduct until the reduct over the whole universe is derived. Compared with five state-of-the-art algorithms over 20 data sets, the experimental results demonstrate that our proposed acceleration strategy can not only significantly accelerate the process of searching reduct but also offer justifiable performance in the task of classification. This study suggests a new trend concerning the problem of quickly deriving reduct. Show more

Keywords: Accelerator, attribute reduction, label-specific guidance, rough set

DOI: 10.3233/JIFS-213112

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1315-1329, 2022

Authors: Saravanan, C. | Anbalagan, P.

Article Type: Research Article

Abstract: Congestion not only affects the power flow, but also leads certain issues, like market power, market inefficiency and security. When the transmission line exceeds their limits congestion is occurred (voltage, thermal, stability). Congestion management is a technique that helps to deal the issue corresponding to congestion. Many methods have been developed to manage congestion, and also several countries execute various strategies for the smooth functioning of their network. In this manuscript, the rescheduling of congestion management in a deregulated environment using DA-MRFO is proposed. The proposed hybrid technique is the combined execution of both the dragonfly algorithm (DA) and manta …ray foraging optimization (MRFO). Dragonfly algorithm is enhanced using Manta ray Foraging optimization (MRFO), hence it is named DA-MRFO technique. The proposed method is used to alleviate transmission grid congestion on group-based electricity market via reprogramming active power of generators and also to reprogram the generator power. Congestion is the major Independent System Operator (ISO) concern on deregulated electricity market that is traditionally controlled by reprogramming generator output power. However, the effects of changes in the generator output power on the overloaded line flow are not identical. All the generators do not represent a desirable approach for congestion management. Here, a generator sensitivity factor is adapted for supporting the optimal generator selection in a congestion management (CM). In a congestion relief process, it is provided at the lowest possible cost. The reduction of power flow with collection of congested lines is probable through coordinated response of reactive energy dispatch as wind farms. The proposed approach is executed in modified IEEE 30 bus system and IEEE 57 bus system, then the efficiency is compared with the various existing optimization approaches. Show more

Keywords: Congestion management, rescheduling, deregulated environment, dragonfly, manta ray foraging optimization

DOI: 10.3233/JIFS-213138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1331-1345, 2022

Authors: Iqbal, Saeed | Qureshi, Adnan N.

Article Type: Research Article

Abstract: Breast cancer diagnosis utilizes histopathological images to get best results as per standards. For detailed diagnosis of breast cancer, microscopic analysis is necessary. During analysis, pathologists examine breast cancer tissues under different magnification levels and it takes a long time, can be hampered by human interpretation and requires expertise of different magnifications. A single patient usually requires dozens of such images during examination. Since, labelling the data is a computationally expensive task, it is assumed that the images for all patients have the same label in conventional image-based classification and is not usually tested practically. In this study, we are …intending to investigate the significance of machine learning techniques in computer aided diagnostic systems based on analysis of histopathological breast cancer images. Publicly available BreakHis data set containing around 8,000 histopathological images of breast tumours is used for conducting experiments. The recently proposed non-parametric approach is proven to show interesting results when compared in detail with machine learning approaches. Our proposed model ’Deep-Hist’ is magnification independent and achieves > 92.46% accuracy with Stochastic Gradient Descent (SGD) which is better than the pretrained models for image classification. Hence, our approach can be used in processing data for use in research and clinical environments to provide second opinions very close to the experts’ intuition. Show more

Keywords: Breast cancer, deep learning, convolutional neural network, batch normalisation, feature selection, classification, histopathology

DOI: 10.3233/JIFS-213158

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1347-1364, 2022

Authors: Cuong, Nguyen Ha Huy | Trinh, Trung Hai | Meesad, Phayung | Nguyen, Thanh Thuy

Article Type: Research Article

Abstract:  A computational method for detecting pineapple ripening could lead to increased agricultural productivity. It is possible to predict fruit maturity before harvesting to increase agricultural productivity. A ripe fruit’s quality, its standard content of physical and chemical properties will increase the value of a good when traded outside the market. This paper studies and improves the Tiny YOLO-v4 model for identifying the pineapple ripening period. Researchers studied pineapples in a pineapple garden in Vietnam’s central region. They wanted to determine when pineapples were ripe. The API and the website are based on the YOLO innovation model. Apps and website APIs …will be available for mobile devices so that people can monitor fruits. Technology transfer and academic research are combined in this study. We prepared the pineapple data set by using 5,000,000 pineapples harvested from the pineapple farm at different stages of growth. To make the measurements, we improved the YOLO-v4 algorithm. This results in a more accurate training model and reduced train-ing time. A 98.26% recognition accuracy is quite impressive. Research takes place at large-scale plantations, so the models are created from the data collected at the plantations and are used as labels; training takes a long time for the tiniest details about pineapples, and finding pineapple-growing regions takes a long time. The deep learning classifier was able to process pineapple plantation photos by using the camera on the mobile phone. Show more

Keywords: Deep learning, computer vision, deep convolutional networks, YOLO, pineapples, segmentation, classifier, loss function

DOI: 10.3233/JIFS-213251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1365-1381, 2022